The present disclosure relates to advection diffusion models, and more specifically, to configuring an advection diffusion model with predetermined rules.
The initialization of circulation models for large water bodies is a very challenging problem that usually requires computing power at the level of supercomputers such as, for example, the BLUE GENE or the IBM POWER computing system by International Business Machines (IBM). In other aspects, the super-computing may be performed in a distributed fashion using a multitude of remotely connected processors across a network. Advanced computing capabilities are often required to compute intricate mathematical models of dynamically changing systems having complex behavioral parameters, which may be tuned to reflect peculiarities with respect to natural phenomena.
For example, in order to tune a model set of parameters for ocean water diffusion, model parameters including turbulence, bed heating exchange, solar radiation etc. are decided. Current methods may include choosing test parameters, running instances of the selected parameters, and analyzing the results to select the optimal set of parameters for the given problem at hand. Running instances of the circulation model is both computationally and time demanding. For example, the selected parameters must be tuned to reflect natural phenomena related to the particular problem. Tuning the model often involves a domain expert to make educated estimations on the physical reality of materials, the conditions of the water body and surroundings, and run various sets set of parameters.
By way of another example, the tracking and forecasting of an oil spill in a water environment may be done with advection-diffusion-reaction equations that are parameter-dependent. As above, the process of parametrization often requires a domain expert that makes educated guesses on possible parameters settings, who then verifies the models with a series of model runs, where each of the runs may have a distinct set of validation data. This common approach for diffusion model configuration can be labor intense, expensive, and computationally complex.